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Piecewise linear classifiers based on nonsmooth optimization approaches

Version 2 2024-06-04, 13:50
Version 1 2019-05-09, 14:59
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posted on 2024-06-04, 13:50 authored by AM Bagirov, R Kasimbeyli, G Öztürk, Julien UgonJulien Ugon
Nonsmooth optimization provides efficient algorithms for solving many machine learning problems. In particular, nonsmooth optimization approaches to supervised data classification problems lead to the design of very efficient algorithms for their solution. In this chapter, we demonstrate how nonsmooth optimization algorithms can be applied to design efficient piecewise linear classifiers for supervised data classification problems. Such classifiers are developed using a max–min and a polyhedral conic separabilities as well as an incremental approach. We report results of numerical experiments and compare the piecewise linear classifiers with a number of other mainstream classifiers.

History

Chapter number

1

Pagination

1-32

ISBN-13

9781493908073

ISBN-10

1493908073

Language

eng

Publication classification

B1.1 Book chapter

Copyright notice

2014, Springer Science+Business Media New York

Extent

29

Editor/Contributor(s)

Rassias T, Floudas C, Butenko S

Publisher

Springer

Place of publication

New York, N.Y.

Title of book

Optimization in science and engineering : in honor of the 60th birthday of Panos M. Pardalos

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